The problem of visualizing scattering objects from far-field data can be addressed by a simple method, named linear sampling method (LSM), which requires the solution of ill-conditioned linear systems. In the present paper we perform a computational and experimental validation of the method, which is implemented by means of four different regularization algorithms. The effectiveness of the LSM when coupled with these algorithms is tested in the case of both simulated and real data. Furthermore a criterion for the choice of a level curve optimally approximating the profile of the scatterers is provided.
Numerical validation of the linear sampling method
PIANA, MICHELE
2002-01-01
Abstract
The problem of visualizing scattering objects from far-field data can be addressed by a simple method, named linear sampling method (LSM), which requires the solution of ill-conditioned linear systems. In the present paper we perform a computational and experimental validation of the method, which is implemented by means of four different regularization algorithms. The effectiveness of the LSM when coupled with these algorithms is tested in the case of both simulated and real data. Furthermore a criterion for the choice of a level curve optimally approximating the profile of the scatterers is provided.
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